Current image or video monitoring usually needs to rely on manual detection and processing of operators. Therefore, although many scenes are covered by cameras, lots of manpower is needed to perform processing and monitoring because there is not intelligent video monitoring method. In addition, when the coverage range of monitoring cameras increases, it is hard to perform efficient processing or respond to emergencies.
Intelligent video monitoring can automatically estimate density of people in a scene and monitor people flow or traffic flow in a video scene in real time based on video data of cameras. Intelligent monitoring has a very wide range of application scenarios. For example, in the aspect of city security, with monitoring of density of people, it is possible to provide alarm when the density of people is too large and deal with the situation in time, to avoid possible emergencies. It is possible to provide crowding situations in real time and provide advises on travel to drivers by performing statistics of number of cars in city roads. It is possible to provide some consumption suggestions and analyses to merchants by performing statistics on people flow in a mall.
Existing intelligent monitoring method for people flow mainly have two approaches. The first one is based on a passenger detection algorithm, but this approach has low accuracy of statistic data for regions with high density of people or cases of severe blocking. In addition, the passenger detection algorithm is time costing itself, and thus cannot achieve the purpose of real-time monitoring. The other approach is independent of passengers. It extracts some features (e.g., edge, texture, or the like) of current image, and performs regression analysis based on those features and some training data to obtain number of passengers in current region. This type of approach usually has low accuracy. Its result is acceptable when density of passengers is large, but when the number of passengers decreases, its error will be large. In addition, the single regression method can hardly differentiate a passenger and a vehicle, and thus have limited application scenarios.